CES 18-42 September, 2018
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We construct a publicly available atlas of children's outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children's earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children's outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $4,200 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child's own Census tract, characteristics of tracts that are one mile away have little predictive power for a child's outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods' causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers' outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children's long- term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.
Keyword: intergenerational social mobility, data tool JEL Classification:
* Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views
of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau's Disclosure Review Board release authorization number CBDRB-FY18-319. We thank John Abowd, Peter Bergman, David Deming, Edward Glaeser, David Grusky, Lawrence Katz, Enrico Moretti, Robert Sampson, Salil Vadhan, and numerous seminar participants for helpful comments and discussions. We are indebted to Caroline Dockes, Michael Droste, Benjamin Goldman, Jack Hoyle, Federico Gonzalez Rodriguez, Jamie Gracie, Matthew Jacob, Martin Koenen, Sarah Merchant, Donato Onorato, Kamelia Stavreva, Wilbur Townsend, Joseph Winkelmann, and other Opportunity Insights pre-doctoral fellows for their outstanding contributions to this work. This research was funded by the Bill and Melinda Gates Foundation, Chan-Zuckerberg Initiative, Robert Wood Johnson Foundation, Kellogg Foundation, and Harvard University.
Cross-sectional statistics on neighborhood characteristics such as poverty rates and job growth have provided a foundation for economic policy and research on labor markets for several decades. In this paper, we have constructed longitudinal statistics that measure children’s outcomes in adulthood based on the Census tract in which they grew up, which can provide an analogous foundation for policies to improve social mobility and research on human capital development.
Using these new statistics, we show that neighborhoods have substantial causal effects on chil- dren’s long-term outcomes at a highly granular level. Moving to a neighborhood that is just a mile or two away can change children’s average earnings by several thousand dollars a year and have significant effects on a spectrum of other outcomes ranging from incarceration to teenage birth rates. Much of this variation in children’s outcomes is unrelated to traditional neighborhood-level proxies for economic success – such as rates of job and wage growth – showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.
Going forward, there are many potential applications of the Opportunity Atlas for both policy and research. Policy makers can use these data to better target programs that aim to improve economic opportunities for disadvantaged children by pinpointing the areas within cities that cur- rently have the weakest outcomes. For example, the placement of pre-school programs or eligibility for local programs or tax credits could potentially be informed by these data. The Atlas can also be used to help low-income families find affordable neighborhoods that offer good opportunities for upward mobility. For example, housing authorities in the Seattle metropolitan area are currently conducting a pilot study that provides information and assistance to housing voucher recipients interested in moving to higher-opportunity areas, motivated by the finding that most voucher recipients currently live in neighborhoods with poor prospects for upward mobility.
For researchers, the Opportunity Atlas offers a new tool to study the determinants of economic opportunity. Comparing areas that have similar observable characteristics but produce different rates of upward mobility using qualitative or quantitative methods can yield new insight into the mechanisms that lead to differences in children’s outcomes. The data could also be used to test and develop better short-term proxies for children’s long-term outcomes beyond conventional indicators such as test scores. And, as tract-level statistics on children’s outcomes are constructed for a longer span of years, they can be used to study the effects of changes in local policies and interventions on
economic mobility. Such research has the potential to yield scalable solutions to increasing mobility out of poverty.
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